154 results found
Eftekhar A, Juffali W, El-Imad J, et al., 2014, Ngram-Derived Pattern Recognition for the Detection and Prediction of Epileptic Seizures, PLOS ONE, Vol: 9, ISSN: 1932-6203
Guven O, Eftekhar A, Hoshyar R, et al., 2014, Realtime ECG Baseline Removal: An Isoelectric Point Estimation Approach, IEEE Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, Pages: 29-32, ISSN: 2163-4025
Leene LB, Constandinou TG, 2014, Ultra-low power design strategy for two-stage amplifier topologies, ELECTRONICS LETTERS, Vol: 50, Pages: 583-584, ISSN: 0013-5194
Luan S, Constandinou TG, 2014, A charge-metering method for voltage-mode neural stimulation, JOURNAL OF NEUROSCIENCE METHODS, Vol: 224, Pages: 39-47, ISSN: 0165-0270
Neuromodulation has wide ranging potential applications in replacing impaired neural function (prosthetics), as a novel form of medical treatment (therapy), and as a tool for investigating neurons and neural function (research). Voltage and current controlled electrical neural stimulation (ENS) are methods that have already been widely applied in both neuroscience and clinical practice for neuroprosthetics. However, there are numerous alternative methods of stimulating or inhibiting neurons. This paper reviews the state-of-the-art in ENS as well as alternative neuromodulation techniques-presenting the operational concepts, technical implementation and limitations-in order to inform system design choices.
Navajas J, Barsakcioglu D, Eftekhar A, et al., 2014, Minimum Requirements for Accurate and Efficient Real-Time On-Chip Spike Sorting, Journal of Neuroscience Methods, Pages: 51-64
Paraskevopoulou SE, Wu D, Eftekhar A, et al., 2014, Hierarchical Adaptive Means (HAM) Clustering for Hardware-Efficient, Unsupervised and Real-time Spike Sorting., Journal of Neuroscience Methods, Vol: 235, Pages: 145-156, ISSN: 1872-678X
This work presents a novel unsupervised algorithm for real-time adaptive clustering of neural spike data (spike sorting). The proposed Hierarchical Adaptive Means (HAM) clustering method combines centroid-based clustering with hierarchical cluster connectivity to classify incoming spikes using groups of clusters. It is described how the proposed method can adaptively track the incoming spike data without requiring any past history, iteration or training and autonomously determines the number of spike classes. Its performance (classification accuracy) has been tested using multiple datasets (both simulated and recorded) achieving a near-identical accuracy compared to k-means (using 10-iterations and provided with the number of spike classes). Also, its robustness in applying to different feature extraction methods has been demonstrated by achieving classification accuracies above 80% across multiple datasets. Last but crucially, its low complexity, that has been quantified through both memory and computation requirements makes this method hugely attractive for future hardware implementation.
Reverter F, Prodromakis T, Liu Y, et al., 2014, Design Considerations for a CMOS Lab-on-Chip Microheater Array to Facilitate the in vitro Thermal Stimulation of Neurons, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 630-633
Shepherd LM, Constandinou TG, Toumazou C, 2014, Towards ultra-low power bio-inspired processing, Body Sensor Networks, Publisher: Springer London, Pages: 273-299, ISBN: 9781447163732
The natural world is analogue and yet the modern microelectronic world with which we interact represents real world data using discrete quantities manipulated by logic. In the human space, we are entering a new wave of body-worn biosensor technology for medical diagnostics and therapy. This new trend is beginning to see the processing interface move back to using continuous quantities, which are more or less in line with the biological processes. We label this computational paradigm “bio-inspired” because of the ability of silicon chip technology which enables the use of inherent device physics, allowing us to approach the computational efficiencies of biology. From a conceptual viewpoint, this has led to a number of more specific morphologies including neuromorphic and retinomorphic processing. These have led scientists to model biological systems such as the cochlea and retina and gain not only superior computational resource efficiency (to conventional hearing aid or camera technology), but also an increased understanding of biological and neurological processes.
Williams I, Constandinou TG, 2014, Computationally efficient modeling of proprioceptive signals in the upper limb for prostheses: a simulation study, FRONTIERS IN NEUROSCIENCE, Vol: 8, ISSN: 1662-453X
Yang Y, Boling S, Eftekhar A, et al., 2014, Computationally Efficient Feature Denoising Filter and Selection of Optimal Features for Noise Insensitive Spike Sorting, 36th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Publisher: IEEE, Pages: 1251-1254, ISSN: 1557-170X
Yoshizaki S, Serb A, Liu Y, et al., 2014, Octagonal CMOS Image Sensor with Strobed RGB LED Illumination for Wireless Capsule Endoscopy, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 1857-1860, ISSN: 0271-4302
Zheng L, Leene LB, Liu Y, et al., 2014, An Adaptive 16/64 kHz, 9-bit SAR ADC with Peak-Aligned Sampling for Neural Spike Recording, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 2385-2388, ISSN: 0271-4302
Barsakcioglu DY, Eftekhar A, Constandinou TG, 2013, Design Optimisation of Front-End Neural Interfaces for Spike Sorting Systems, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 2501-2504, ISSN: 0271-4302
Koutsos E, Paraskevopoulou SE, Constandinou TG, 2013, A 1.5μW NEO-based Spike Detector with Adaptive-Threshold for Calibration-free Multichannel Neural Interfaces, IEEE International Symposium on Circuits and Systems (ISCAS)
This paper presents a novel front-end circuit for detecting action potentials in extracellular neural recordings. By implementing a real-time, adaptive algorithm to determine an effective threshold for robustly detecting a spike, the need for calibration and/or external monitoring is eliminated. The input signal is first pre-processed by utilising a non-linear energy operator (NEO) to effectively boost the signal-to-noise ratio (SNR) of the spike feature of interest. The spike detection threshold is then determined by tracking the peak NEO response and applying a non-linear gain to realise an adaptive response to different spike amplitudes and background noise levels. The proposed algorithm and its implementation is shown to achieve both accurate and robust spike detection, by minimising falsely detected spikes and/or missed spikes. The system has been implemented in a commercially available 0.18μm technology requiring a total power consumption of 1.5μW from a 1.8V supply and occupying a compact footprint of only 0.03$\,$mm$^2$ silicon area. The proposed circuit is thus ideally suited for high-channel count, calibration-free, neural interfaces.
Leene LB, Liu Y, Constandinou TG, 2013, A Compact Recording Array for Neural Interfaces, IEEE Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, Pages: 97-100, ISSN: 2163-4025
Leene LB, Luan S, Constandinou TG, 2013, A 890fJ/bit UWB transmitter for SOC integration in high bit-rate transcutaneous bio-implants, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 2271-2274, ISSN: 0271-4302
Paraskevopoulou SE, Barsakcioglu DY, Saberi MR, et al., 2013, Feature extraction using first and second derivative extrema (FSDE) for real-time and hardware-efficient spike sorting, JOURNAL OF NEUROSCIENCE METHODS, Vol: 215, Pages: 29-37, ISSN: 0165-0270
Williams I, Constandinou TG, 2013, An Energy-Efficient, Dynamic Voltage Scaling Neural Stimulator for a Proprioceptive Prosthesis, IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, Vol: 7, Pages: 129-139, ISSN: 1932-4545
Williams I, Constandinou TG, 2013, Modelling muscle spindle dynamics for a proprioceptive prosthesis, Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), Publisher: IEEE
Muscle spindles are found throughout our skeletalmuscle tissue and continuously provide us with a sense of our limbs position and motion (proprioception). This paper advances a model for generating artificial muscle spindle signalsfor a prosthetic limb, with the aim of one day providing amputees with a sense of feeling in their artificial limb. By utilising the Opensim biomechanical modelling package the relationship between a joints angle and the length of surrounding muscles is estimated for a prosthetic limb. This is then applied to the established Mileusnic model to determine the associated muscle spindle firing pattern. This complete system model is then reduced to allow for a computationallyefficient hardware implementation. This reduction is achieved with minimal impact on accuracy by selecting key monoarticular muscles and fitting equations to relate joint angle to muscle length. Parameter values fitting the Mileusnic modelto human spindles are then proposed and validated against previously published human neural recordings. Finally, a model for fusimotor signals is also proposed based on data previously recorded from reduced animal experiments.
Woods SP, Constandinou TG, 2013, Wireless Capsule Endoscope for Targeted Drug Delivery: Mechanics and Design Considerations, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, Vol: 60, Pages: 945-953, ISSN: 0018-9294
Constandinou TG, Hafliger P, 2012, Guest Editorial-Special Issue on Selected Papers From BioCAS 2011, IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, Vol: 6, Pages: 401-402, ISSN: 1932-4545
Guilvard A, Eftekhar A, Luan S, et al., 2012, A Fully-Programmable Neural Interface for Multi-Polar, Multi-Channel Stimulation Strategies, IEEE International Symposium on Circuits and Systems, Publisher: IEEE, Pages: 2235-2238, ISSN: 0271-4302
Haaheim B, Constandinou TG, 2012, A Sub-1μW, 16kHz Current-Mode SAR-ADC for Neural Spike Recording, International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, ISSN: 0271-4302
This paper presents an ultra-low-power 8-bit asynchronous current-mode (CM) successive approximation (SAR) analogue-to-digital converter (ADC) for single-neuron spike recording. The novel design exploits CM techniques to support operation at supply voltages down to 1.2V, consuming under500nA at 16kSamples/s. The design features easy scalability, and allows for a tuneable sampling frequency and dynamic range (DR). The circuit is designed in a commercially-available 0.18u mCMOS technology and occupies a chip area of 0.078 sq.mm. The system requires a single, post-fabrication current calibration supportedby on-chip circuitry to ensure robust operation through process and mismatch variations.
Luan S, Constandinou TG, 2012, A Novel Charge-Metering Method for Voltage Mode Neural Stimulation, IEEE International Symposium on Circuits and Systems, Publisher: IEEE, Pages: 2239-2242, ISSN: 0271-4302
Mirza KB, Luan S, Eftekhar A, et al., 2012, Towards a Fully-Integrated Solution for Capacitor-based Neural Stimulation, IEEE International Symposium on Circuits and Systems, Publisher: IEEE, Pages: 2243-2246, ISSN: 0271-4302
Paraskevopoulou SE, Constandinou TG, 2012, An Ultra-Low-Power Front-end Neural Interface with Automatic Gain for Uncalibrated Monitoring, IEEE International Symposium on Circuits and Systems, Publisher: IEEE, Pages: 193-196, ISSN: 0271-4302
Williams I, Constandinou TG, 2012, An Energy-Efficient, Dynamic Voltage Scaling Neural Stimulator for a Proprioceptive Prosthesis, IEEE International Symposium on Circuits and Systems, Publisher: IEEE, Pages: 1091-1094, ISSN: 0271-4302
Abshire P, Bermak A, Berner R, et al., 2011, Confession session: Learning from others mistakes., Publisher: IEEE, Pages: 1149-1162
Georgiou J, Andreou AG, 2011, Guest Editorial—Special Issue on Selected Papers From BioCAS 2010, IEEE Transactions on Biomedical Circuits and Systems, Vol: 5, Pages: 401-402, ISSN: 1932-4545
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